Overview

Dataset statistics

Number of variables17
Number of observations2460
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory410.5 KiB
Average record size in memory170.9 B

Variable types

Numeric8
Categorical9

Alerts

year has constant value "2016"Constant
date has a high cardinality: 203 distinct valuesHigh cardinality
start_time has a high cardinality: 207 distinct valuesHigh cardinality
away_team_hits is highly overall correlated with away_team_runsHigh correlation
away_team_runs is highly overall correlated with away_team_hitsHigh correlation
home_team_hits is highly overall correlated with home_team_runsHigh correlation
home_team_runs is highly overall correlated with home_team_hitsHigh correlation
game_type is highly overall correlated with week_dayHigh correlation
home_team is highly overall correlated with venue and 1 other fieldsHigh correlation
venue is highly overall correlated with home_team and 1 other fieldsHigh correlation
week_day is highly overall correlated with game_typeHigh correlation
fiedl_type is highly overall correlated with home_team and 1 other fieldsHigh correlation
fiedl_type is highly imbalanced (64.2%)Imbalance
away_team is uniformly distributedUniform
home_team is uniformly distributedUniform
away_team_errors has 1407 (57.2%) zerosZeros
away_team_runs has 156 (6.3%) zerosZeros
home_team_errors has 1416 (57.6%) zerosZeros
home_team_runs has 130 (5.3%) zerosZeros

Reproduction

Analysis started2023-02-04 00:51:04.342784
Analysis finished2023-02-04 00:51:16.572065
Duration12.23 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

attendance
Real number (ℝ)

Distinct2374
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30370.704
Minimum8766
Maximum54449
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.4 KiB
2023-02-03T18:51:16.644066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum8766
5-th percentile13971.15
Q122432
median30604.5
Q338396.25
95-th percentile45624.1
Maximum54449
Range45683
Interquartile range (IQR)15964.25

Descriptive statistics

Standard deviation9875.4667
Coefficient of variation (CV)0.32516424
Kurtosis-0.90285907
Mean30370.704
Median Absolute Deviation (MAD)8006
Skewness-0.052483633
Sum74711931
Variance97524843
MonotonicityNot monotonic
2023-02-03T18:51:16.754066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27631 3
 
0.1%
41210 2
 
0.1%
41850 2
 
0.1%
13481 2
 
0.1%
44317 2
 
0.1%
34294 2
 
0.1%
39691 2
 
0.1%
36544 2
 
0.1%
22230 2
 
0.1%
26087 2
 
0.1%
Other values (2364) 2439
99.1%
ValueCountFrequency (%)
8766 1
< 0.1%
9393 1
< 0.1%
9890 1
< 0.1%
10068 1
< 0.1%
10072 1
< 0.1%
10114 1
< 0.1%
10115 1
< 0.1%
10117 1
< 0.1%
10251 1
< 0.1%
10283 1
< 0.1%
ValueCountFrequency (%)
54449 2
0.1%
54269 1
< 0.1%
53901 1
< 0.1%
53621 1
< 0.1%
53449 1
< 0.1%
53409 1
< 0.1%
53299 1
< 0.1%
53297 1
< 0.1%
53279 1
< 0.1%
52728 1
< 0.1%

away_team
Categorical

Distinct30
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size38.4 KiB
Chicago Cubs
 
90
Los Angeles Dodgers
 
87
Cleveland Indians
 
86
Toronto Blue Jays
 
85
San Francisco Giants
 
84
Other values (25)
2028 

Length

Max length29
Median length19
Mean length16.694309
Min length12

Characters and Unicode

Total characters41068
Distinct characters46
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNew York Mets
2nd rowPhiladelphia Phillies
3rd rowMinnesota Twins
4th rowWashington Nationals
5th rowColorado Rockies

Common Values

ValueCountFrequency (%)
Chicago Cubs 90
 
3.7%
Los Angeles Dodgers 87
 
3.5%
Cleveland Indians 86
 
3.5%
Toronto Blue Jays 85
 
3.5%
San Francisco Giants 84
 
3.4%
Boston Red Sox 83
 
3.4%
Washington Nationals 83
 
3.4%
Baltimore Orioles 82
 
3.3%
Texas Rangers 82
 
3.3%
Cincinnati Reds 81
 
3.3%
Other values (20) 1617
65.7%

Length

2023-02-03T18:51:16.859066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chicago 171
 
2.8%
angeles 168
 
2.8%
los 168
 
2.8%
san 165
 
2.7%
sox 164
 
2.7%
new 161
 
2.7%
york 161
 
2.7%
cubs 90
 
1.5%
dodgers 87
 
1.4%
cleveland 86
 
1.4%
Other values (57) 4646
76.6%

Most occurring characters

ValueCountFrequency (%)
a 3762
 
9.2%
3607
 
8.8%
s 3530
 
8.6%
e 3275
 
8.0%
i 3021
 
7.4%
o 2800
 
6.8%
n 2714
 
6.6%
t 2035
 
5.0%
r 1876
 
4.6%
l 1804
 
4.4%
Other values (36) 12644
30.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31395
76.4%
Uppercase Letter 5986
 
14.6%
Space Separator 3607
 
8.8%
Other Punctuation 80
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3762
12.0%
s 3530
11.2%
e 3275
10.4%
i 3021
9.6%
o 2800
8.9%
n 2714
8.6%
t 2035
 
6.5%
r 1876
 
6.0%
l 1804
 
5.7%
g 915
 
2.9%
Other values (14) 5663
18.0%
Uppercase Letter
ValueCountFrequency (%)
C 670
11.2%
A 653
10.9%
B 492
 
8.2%
S 490
 
8.2%
R 489
 
8.2%
M 485
 
8.1%
T 410
 
6.8%
P 405
 
6.8%
D 330
 
5.5%
L 248
 
4.1%
Other values (10) 1314
22.0%
Space Separator
ValueCountFrequency (%)
3607
100.0%
Other Punctuation
ValueCountFrequency (%)
. 80
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 37381
91.0%
Common 3687
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3762
 
10.1%
s 3530
 
9.4%
e 3275
 
8.8%
i 3021
 
8.1%
o 2800
 
7.5%
n 2714
 
7.3%
t 2035
 
5.4%
r 1876
 
5.0%
l 1804
 
4.8%
g 915
 
2.4%
Other values (34) 11649
31.2%
Common
ValueCountFrequency (%)
3607
97.8%
. 80
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41068
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3762
 
9.2%
3607
 
8.8%
s 3530
 
8.6%
e 3275
 
8.0%
i 3021
 
7.4%
o 2800
 
6.8%
n 2714
 
6.6%
t 2035
 
5.0%
r 1876
 
4.6%
l 1804
 
4.4%
Other values (36) 12644
30.8%

away_team_errors
Real number (ℝ)

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5800813
Minimum0
Maximum5
Zeros1407
Zeros (%)57.2%
Negative0
Negative (%)0.0%
Memory size38.4 KiB
2023-02-03T18:51:16.942065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.79322671
Coefficient of variation (CV)1.3674406
Kurtosis2.1811828
Mean0.5800813
Median Absolute Deviation (MAD)0
Skewness1.4582673
Sum1427
Variance0.62920861
MonotonicityNot monotonic
2023-02-03T18:51:17.019066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1407
57.2%
1 765
31.1%
2 215
 
8.7%
3 61
 
2.5%
4 11
 
0.4%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 1407
57.2%
1 765
31.1%
2 215
 
8.7%
3 61
 
2.5%
4 11
 
0.4%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 11
 
0.4%
3 61
 
2.5%
2 215
 
8.7%
1 765
31.1%
0 1407
57.2%

away_team_hits
Real number (ℝ)

Distinct22
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.7670732
Minimum1
Maximum22
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.4 KiB
2023-02-03T18:51:17.106066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q16
median8
Q311
95-th percentile15
Maximum22
Range21
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.5126873
Coefficient of variation (CV)0.40066819
Kurtosis0.13926584
Mean8.7670732
Median Absolute Deviation (MAD)2
Skewness0.51233243
Sum21567
Variance12.338972
MonotonicityNot monotonic
2023-02-03T18:51:17.190066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
9 293
11.9%
7 287
11.7%
8 275
11.2%
10 238
9.7%
6 225
9.1%
5 197
8.0%
11 194
7.9%
4 135
 
5.5%
12 132
 
5.4%
13 113
 
4.6%
Other values (12) 371
15.1%
ValueCountFrequency (%)
1 7
 
0.3%
2 26
 
1.1%
3 83
 
3.4%
4 135
5.5%
5 197
8.0%
6 225
9.1%
7 287
11.7%
8 275
11.2%
9 293
11.9%
10 238
9.7%
ValueCountFrequency (%)
22 4
 
0.2%
21 1
 
< 0.1%
20 2
 
0.1%
19 12
 
0.5%
18 15
 
0.6%
17 28
 
1.1%
16 40
 
1.6%
15 66
2.7%
14 87
3.5%
13 113
4.6%

away_team_runs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4150407
Minimum0
Maximum21
Zeros156
Zeros (%)6.3%
Negative0
Negative (%)0.0%
Memory size38.4 KiB
2023-02-03T18:51:17.276066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q36
95-th percentile10
Maximum21
Range21
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.1053905
Coefficient of variation (CV)0.70336622
Kurtosis1.0089307
Mean4.4150407
Median Absolute Deviation (MAD)2
Skewness0.93889878
Sum10861
Variance9.64345
MonotonicityNot monotonic
2023-02-03T18:51:17.356066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
3 347
14.1%
2 340
13.8%
4 308
12.5%
5 272
11.1%
1 265
10.8%
6 217
8.8%
7 179
7.3%
0 156
6.3%
8 115
 
4.7%
9 85
 
3.5%
Other values (10) 176
7.2%
ValueCountFrequency (%)
0 156
6.3%
1 265
10.8%
2 340
13.8%
3 347
14.1%
4 308
12.5%
5 272
11.1%
6 217
8.8%
7 179
7.3%
8 115
 
4.7%
9 85
 
3.5%
ValueCountFrequency (%)
21 1
 
< 0.1%
18 1
 
< 0.1%
17 1
 
< 0.1%
16 5
 
0.2%
15 10
 
0.4%
14 7
 
0.3%
13 21
 
0.9%
12 25
 
1.0%
11 36
1.5%
10 69
2.8%

date
Categorical

Distinct203
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size38.4 KiB
June 25
 
16
May 11
 
16
September 17
 
16
August 16
 
16
May 7
 
16
Other values (198)
2380 

Length

Max length12
Median length10
Mean length7.9321138
Min length5

Characters and Unicode

Total characters19513
Distinct characters34
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)0.7%

Sample

1st rowApril 3
2nd rowApril 6
3rd rowApril 6
4th rowApril 6
5th rowApril 6

Common Values

ValueCountFrequency (%)
June 25 16
 
0.7%
May 11 16
 
0.7%
September 17 16
 
0.7%
August 16 16
 
0.7%
May 7 16
 
0.7%
May 18 16
 
0.7%
July 20 16
 
0.7%
May 14 16
 
0.7%
August 31 16
 
0.7%
September 14 15
 
0.6%
Other values (193) 2301
93.5%

Length

2023-02-03T18:51:17.447069image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
august 424
 
8.6%
may 423
 
8.6%
september 408
 
8.3%
june 406
 
8.3%
july 380
 
7.7%
april 354
 
7.2%
17 91
 
1.8%
24 90
 
1.8%
10 89
 
1.8%
20 87
 
1.8%
Other values (29) 2168
44.1%

Most occurring characters

ValueCountFrequency (%)
2460
 
12.6%
e 1697
 
8.7%
u 1634
 
8.4%
1 1053
 
5.4%
2 1050
 
5.4%
t 895
 
4.6%
r 827
 
4.2%
y 803
 
4.1%
J 786
 
4.0%
A 778
 
4.0%
Other values (24) 7530
38.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 10396
53.3%
Decimal Number 4197
21.5%
Space Separator 2460
 
12.6%
Uppercase Letter 2460
 
12.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 1697
16.3%
u 1634
15.7%
t 895
8.6%
r 827
8.0%
y 803
7.7%
p 762
7.3%
l 734
7.1%
b 473
 
4.5%
g 424
 
4.1%
s 424
 
4.1%
Other values (7) 1723
16.6%
Decimal Number
ValueCountFrequency (%)
1 1053
25.1%
2 1050
25.0%
3 360
 
8.6%
0 261
 
6.2%
7 260
 
6.2%
9 248
 
5.9%
4 247
 
5.9%
5 246
 
5.9%
6 241
 
5.7%
8 231
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
J 786
32.0%
A 778
31.6%
M 423
17.2%
S 408
16.6%
O 63
 
2.6%
N 2
 
0.1%
Space Separator
ValueCountFrequency (%)
2460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 12856
65.9%
Common 6657
34.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 1697
13.2%
u 1634
12.7%
t 895
 
7.0%
r 827
 
6.4%
y 803
 
6.2%
J 786
 
6.1%
A 778
 
6.1%
p 762
 
5.9%
l 734
 
5.7%
b 473
 
3.7%
Other values (13) 3467
27.0%
Common
ValueCountFrequency (%)
2460
37.0%
1 1053
15.8%
2 1050
15.8%
3 360
 
5.4%
0 261
 
3.9%
7 260
 
3.9%
9 248
 
3.7%
4 247
 
3.7%
5 246
 
3.7%
6 241
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19513
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2460
 
12.6%
e 1697
 
8.7%
u 1634
 
8.4%
1 1053
 
5.4%
2 1050
 
5.4%
t 895
 
4.6%
r 827
 
4.2%
y 803
 
4.1%
J 786
 
4.0%
A 778
 
4.0%
Other values (24) 7530
38.6%

game_duration
Real number (ℝ)

Distinct168
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean288.63333
Minimum115
Maximum613
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size38.4 KiB
2023-02-03T18:51:17.552066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum115
5-th percentile229
Q1247
median302
Q3319
95-th percentile352
Maximum613
Range498
Interquartile range (IQR)72

Descriptive statistics

Standard deviation49.449673
Coefficient of variation (CV)0.1713235
Kurtosis3.3939088
Mean288.63333
Median Absolute Deviation (MAD)44
Skewness1.1491938
Sum710038
Variance2445.2701
MonotonicityNot monotonic
2023-02-03T18:51:17.662066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
255 55
 
2.2%
256 54
 
2.2%
300 53
 
2.2%
254 51
 
2.1%
308 47
 
1.9%
304 47
 
1.9%
303 46
 
1.9%
305 46
 
1.9%
252 45
 
1.8%
307 45
 
1.8%
Other values (158) 1971
80.1%
ValueCountFrequency (%)
115 1
 
< 0.1%
155 1
 
< 0.1%
202 1
 
< 0.1%
206 1
 
< 0.1%
207 1
 
< 0.1%
208 2
0.1%
210 4
0.2%
211 3
0.1%
212 3
0.1%
213 2
0.1%
ValueCountFrequency (%)
613 1
< 0.1%
556 1
< 0.1%
548 1
< 0.1%
547 1
< 0.1%
534 1
< 0.1%
526 1
< 0.1%
525 2
0.1%
523 1
< 0.1%
518 1
< 0.1%
510 1
< 0.1%

game_type
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.4 KiB
Night Game
1664 
Day Game
796 

Length

Max length10
Median length10
Mean length9.3528455
Min length8

Characters and Unicode

Total characters23008
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowNight Game
2nd rowNight Game
3rd rowNight Game
4th rowNight Game
5th rowDay Game

Common Values

ValueCountFrequency (%)
Night Game 1664
67.6%
Day Game 796
32.4%

Length

2023-02-03T18:51:17.764066image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-03T18:51:17.870030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
game 2460
50.0%
night 1664
33.8%
day 796
 
16.2%

Most occurring characters

ValueCountFrequency (%)
a 3256
14.2%
2460
10.7%
G 2460
10.7%
m 2460
10.7%
e 2460
10.7%
N 1664
7.2%
i 1664
7.2%
g 1664
7.2%
h 1664
7.2%
t 1664
7.2%
Other values (2) 1592
6.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15628
67.9%
Uppercase Letter 4920
 
21.4%
Space Separator 2460
 
10.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3256
20.8%
m 2460
15.7%
e 2460
15.7%
i 1664
10.6%
g 1664
10.6%
h 1664
10.6%
t 1664
10.6%
y 796
 
5.1%
Uppercase Letter
ValueCountFrequency (%)
G 2460
50.0%
N 1664
33.8%
D 796
 
16.2%
Space Separator
ValueCountFrequency (%)
2460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 20548
89.3%
Common 2460
 
10.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3256
15.8%
G 2460
12.0%
m 2460
12.0%
e 2460
12.0%
N 1664
8.1%
i 1664
8.1%
g 1664
8.1%
h 1664
8.1%
t 1664
8.1%
D 796
 
3.9%
Common
ValueCountFrequency (%)
2460
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23008
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3256
14.2%
2460
10.7%
G 2460
10.7%
m 2460
10.7%
e 2460
10.7%
N 1664
7.2%
i 1664
7.2%
g 1664
7.2%
h 1664
7.2%
t 1664
7.2%
Other values (2) 1592
6.9%

home_team
Categorical

HIGH CORRELATION  UNIFORM 

Distinct30
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size38.4 KiB
Cleveland Indians
 
89
Chicago Cubs
 
89
Los Angeles Dodgers
 
86
Toronto Blue Jays
 
86
Washington Nationals
 
84
Other values (25)
2026 

Length

Max length29
Median length19
Mean length16.695528
Min length12

Characters and Unicode

Total characters41071
Distinct characters46
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKansas City Royals
2nd rowCincinnati Reds
3rd rowBaltimore Orioles
4th rowAtlanta Braves
5th rowArizona Diamondbacks

Common Values

ValueCountFrequency (%)
Cleveland Indians 89
 
3.6%
Chicago Cubs 89
 
3.6%
Los Angeles Dodgers 86
 
3.5%
Toronto Blue Jays 86
 
3.5%
Washington Nationals 84
 
3.4%
Texas Rangers 83
 
3.4%
San Francisco Giants 83
 
3.4%
Boston Red Sox 82
 
3.3%
Kansas City Royals 81
 
3.3%
Cincinnati Reds 81
 
3.3%
Other values (20) 1616
65.7%

Length

2023-02-03T18:51:17.951030image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
chicago 169
 
2.8%
los 167
 
2.8%
angeles 167
 
2.8%
san 164
 
2.7%
sox 162
 
2.7%
new 162
 
2.7%
york 162
 
2.7%
indians 89
 
1.5%
cleveland 89
 
1.5%
cubs 89
 
1.5%
Other values (57) 4646
76.6%

Most occurring characters

ValueCountFrequency (%)
a 3770
 
9.2%
3606
 
8.8%
s 3530
 
8.6%
e 3277
 
8.0%
i 3014
 
7.3%
o 2795
 
6.8%
n 2724
 
6.6%
t 2033
 
4.9%
r 1872
 
4.6%
l 1810
 
4.4%
Other values (36) 12640
30.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 31399
76.5%
Uppercase Letter 5985
 
14.6%
Space Separator 3606
 
8.8%
Other Punctuation 81
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3770
12.0%
s 3530
11.2%
e 3277
10.4%
i 3014
9.6%
o 2795
8.9%
n 2724
8.7%
t 2033
 
6.5%
r 1872
 
6.0%
l 1810
 
5.8%
d 913
 
2.9%
Other values (14) 5661
18.0%
Uppercase Letter
ValueCountFrequency (%)
C 671
11.2%
A 653
10.9%
B 492
 
8.2%
R 489
 
8.2%
S 488
 
8.2%
M 484
 
8.1%
T 411
 
6.9%
P 403
 
6.7%
D 328
 
5.5%
L 248
 
4.1%
Other values (10) 1318
22.0%
Space Separator
ValueCountFrequency (%)
3606
100.0%
Other Punctuation
ValueCountFrequency (%)
. 81
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 37384
91.0%
Common 3687
 
9.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3770
 
10.1%
s 3530
 
9.4%
e 3277
 
8.8%
i 3014
 
8.1%
o 2795
 
7.5%
n 2724
 
7.3%
t 2033
 
5.4%
r 1872
 
5.0%
l 1810
 
4.8%
d 913
 
2.4%
Other values (34) 11646
31.2%
Common
ValueCountFrequency (%)
3606
97.8%
. 81
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41071
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 3770
 
9.2%
3606
 
8.8%
s 3530
 
8.6%
e 3277
 
8.0%
i 3014
 
7.3%
o 2795
 
6.8%
n 2724
 
6.6%
t 2033
 
4.9%
r 1872
 
4.6%
l 1810
 
4.4%
Other values (36) 12640
30.8%

home_team_errors
Real number (ℝ)

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.58617886
Minimum0
Maximum5
Zeros1416
Zeros (%)57.6%
Negative0
Negative (%)0.0%
Memory size38.4 KiB
2023-02-03T18:51:18.031025image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum5
Range5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.80581712
Coefficient of variation (CV)1.3746949
Kurtosis2.0546943
Mean0.58617886
Median Absolute Deviation (MAD)0
Skewness1.4410193
Sum1442
Variance0.64934123
MonotonicityNot monotonic
2023-02-03T18:51:18.109905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 1416
57.6%
1 732
29.8%
2 241
 
9.8%
3 57
 
2.3%
4 13
 
0.5%
5 1
 
< 0.1%
ValueCountFrequency (%)
0 1416
57.6%
1 732
29.8%
2 241
 
9.8%
3 57
 
2.3%
4 13
 
0.5%
5 1
 
< 0.1%
ValueCountFrequency (%)
5 1
 
< 0.1%
4 13
 
0.5%
3 57
 
2.3%
2 241
 
9.8%
1 732
29.8%
0 1416
57.6%

home_team_hits
Real number (ℝ)

Distinct23
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6113821
Minimum0
Maximum22
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size38.4 KiB
2023-02-03T18:51:18.196905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4
Q16
median8
Q311
95-th percentile15
Maximum22
Range22
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.4386792
Coefficient of variation (CV)0.39931792
Kurtosis0.18147362
Mean8.6113821
Median Absolute Deviation (MAD)2
Skewness0.47623304
Sum21184
Variance11.824515
MonotonicityNot monotonic
2023-02-03T18:51:18.280906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
8 294
12.0%
7 275
11.2%
9 274
11.1%
6 257
10.4%
10 236
9.6%
11 194
7.9%
5 184
7.5%
12 165
6.7%
4 151
6.1%
13 96
 
3.9%
Other values (13) 334
13.6%
ValueCountFrequency (%)
0 1
 
< 0.1%
1 12
 
0.5%
2 33
 
1.3%
3 72
 
2.9%
4 151
6.1%
5 184
7.5%
6 257
10.4%
7 275
11.2%
8 294
12.0%
9 274
11.1%
ValueCountFrequency (%)
22 1
 
< 0.1%
21 3
 
0.1%
20 1
 
< 0.1%
19 10
 
0.4%
18 17
 
0.7%
17 28
 
1.1%
16 31
 
1.3%
15 36
 
1.5%
14 89
3.6%
13 96
3.9%

home_team_runs
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5203252
Minimum0
Maximum17
Zeros130
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size38.4 KiB
2023-02-03T18:51:18.363905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median4
Q36
95-th percentile11
Maximum17
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.1125024
Coefficient of variation (CV)0.68855719
Kurtosis0.83058099
Mean4.5203252
Median Absolute Deviation (MAD)2
Skewness0.92009549
Sum11120
Variance9.6876713
MonotonicityNot monotonic
2023-02-03T18:51:18.446905image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
3 354
14.4%
2 312
12.7%
4 305
12.4%
5 301
12.2%
1 273
11.1%
6 207
8.4%
7 190
7.7%
0 130
 
5.3%
8 130
 
5.3%
9 79
 
3.2%
Other values (8) 179
7.3%
ValueCountFrequency (%)
0 130
 
5.3%
1 273
11.1%
2 312
12.7%
3 354
14.4%
4 305
12.4%
5 301
12.2%
6 207
8.4%
7 190
7.7%
8 130
 
5.3%
9 79
 
3.2%
ValueCountFrequency (%)
17 4
 
0.2%
16 5
 
0.2%
15 4
 
0.2%
14 16
 
0.7%
13 26
 
1.1%
12 33
 
1.3%
11 37
 
1.5%
10 54
2.2%
9 79
3.2%
8 130
5.3%

start_time
Categorical

Distinct207
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Memory size38.4 KiB
7:10pm
413 
7:11pm
213 
7:07pm
195 
7:08pm
 
138
1:10pm
 
121
Other values (202)
1380 

Length

Max length8
Median length7
Mean length7.0512195
Min length7

Characters and Unicode

Total characters17346
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79 ?
Unique (%)3.2%

Sample

1st row 7:38pm
2nd row 7:11pm
3rd row 7:07pm
4th row 7:10pm
5th row 12:40pm

Common Values

ValueCountFrequency (%)
7:10pm 413
 
16.8%
7:11pm 213
 
8.7%
7:07pm 195
 
7.9%
7:08pm 138
 
5.6%
1:10pm 121
 
4.9%
1:11pm 82
 
3.3%
7:09pm 80
 
3.3%
7:15pm 66
 
2.7%
6:40pm 51
 
2.1%
7:06pm 50
 
2.0%
Other values (197) 1051
42.7%

Length

2023-02-03T18:51:18.542902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
7:10pm 413
 
16.8%
7:11pm 213
 
8.7%
7:07pm 195
 
7.9%
7:08pm 138
 
5.6%
1:10pm 121
 
4.9%
1:11pm 82
 
3.3%
7:09pm 80
 
3.3%
7:15pm 66
 
2.7%
6:40pm 51
 
2.1%
7:06pm 50
 
2.0%
Other values (197) 1051
42.7%

Most occurring characters

ValueCountFrequency (%)
2460
14.2%
: 2460
14.2%
m 2460
14.2%
p 2458
14.2%
1 2401
13.8%
7 1678
9.7%
0 1476
8.5%
6 432
 
2.5%
2 358
 
2.1%
4 287
 
1.7%
Other values (5) 876
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7506
43.3%
Lowercase Letter 4920
28.4%
Space Separator 2460
 
14.2%
Other Punctuation 2460
 
14.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2401
32.0%
7 1678
22.4%
0 1476
19.7%
6 432
 
5.8%
2 358
 
4.8%
4 287
 
3.8%
8 286
 
3.8%
5 230
 
3.1%
3 206
 
2.7%
9 152
 
2.0%
Lowercase Letter
ValueCountFrequency (%)
m 2460
50.0%
p 2458
50.0%
a 2
 
< 0.1%
Space Separator
ValueCountFrequency (%)
2460
100.0%
Other Punctuation
ValueCountFrequency (%)
: 2460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12426
71.6%
Latin 4920
 
28.4%

Most frequent character per script

Common
ValueCountFrequency (%)
2460
19.8%
: 2460
19.8%
1 2401
19.3%
7 1678
13.5%
0 1476
11.9%
6 432
 
3.5%
2 358
 
2.9%
4 287
 
2.3%
8 286
 
2.3%
5 230
 
1.9%
Other values (2) 358
 
2.9%
Latin
ValueCountFrequency (%)
m 2460
50.0%
p 2458
50.0%
a 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17346
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2460
14.2%
: 2460
14.2%
m 2460
14.2%
p 2458
14.2%
1 2401
13.8%
7 1678
9.7%
0 1476
8.5%
6 432
 
2.5%
2 358
 
2.1%
4 287
 
1.7%
Other values (5) 876
 
5.1%

venue
Categorical

Distinct31
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size38.4 KiB
Progressive Field
 
89
Wrigley Field
 
89
Dodger Stadium
 
86
Rogers Centre
 
86
Nationals Park
 
84
Other values (26)
2026 

Length

Max length32
Median length20
Mean length16.528049
Min length9

Characters and Unicode

Total characters40659
Distinct characters46
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row Kauffman Stadium
2nd row Great American Ball Park
3rd row Oriole Park at Camden Yards
4th row Turner Field
5th row Chase Field

Common Values

ValueCountFrequency (%)
Progressive Field 89
 
3.6%
Wrigley Field 89
 
3.6%
Dodger Stadium 86
 
3.5%
Rogers Centre 86
 
3.5%
Nationals Park 84
 
3.4%
Globe Life Park in Arlington 83
 
3.4%
AT&T Park 83
 
3.4%
Fenway Park 82
 
3.3%
Kauffman Stadium 81
 
3.3%
Great American Ball Park 81
 
3.3%
Other values (21) 1616
65.7%

Length

2023-02-03T18:51:18.633904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
park 1059
 
17.0%
field 824
 
13.2%
stadium 410
 
6.6%
iii 162
 
2.6%
progressive 89
 
1.4%
wrigley 89
 
1.4%
rogers 86
 
1.4%
centre 86
 
1.4%
dodger 86
 
1.4%
nationals 84
 
1.4%
Other values (42) 3247
52.2%

Most occurring characters

ValueCountFrequency (%)
6222
15.3%
a 3661
 
9.0%
e 3300
 
8.1%
i 2877
 
7.1%
r 2717
 
6.7%
l 2212
 
5.4%
d 1725
 
4.2%
n 1633
 
4.0%
o 1321
 
3.2%
t 1312
 
3.2%
Other values (36) 13679
33.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 27325
67.2%
Uppercase Letter 6788
 
16.7%
Space Separator 6222
 
15.3%
Other Punctuation 243
 
0.6%
Dash Punctuation 81
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 3661
13.4%
e 3300
12.1%
i 2877
10.5%
r 2717
9.9%
l 2212
8.1%
d 1725
 
6.3%
n 1633
 
6.0%
o 1321
 
4.8%
t 1312
 
4.8%
k 1302
 
4.8%
Other values (13) 5265
19.3%
Uppercase Letter
ValueCountFrequency (%)
P 1309
19.3%
F 907
13.4%
C 893
13.2%
S 571
8.4%
A 490
 
7.2%
I 486
 
7.2%
T 408
 
6.0%
M 323
 
4.8%
B 244
 
3.6%
N 164
 
2.4%
Other values (9) 993
14.6%
Other Punctuation
ValueCountFrequency (%)
. 160
65.8%
& 83
34.2%
Space Separator
ValueCountFrequency (%)
6222
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 81
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 34113
83.9%
Common 6546
 
16.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 3661
 
10.7%
e 3300
 
9.7%
i 2877
 
8.4%
r 2717
 
8.0%
l 2212
 
6.5%
d 1725
 
5.1%
n 1633
 
4.8%
o 1321
 
3.9%
t 1312
 
3.8%
P 1309
 
3.8%
Other values (32) 12046
35.3%
Common
ValueCountFrequency (%)
6222
95.1%
. 160
 
2.4%
& 83
 
1.3%
- 81
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40659
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6222
15.3%
a 3661
 
9.0%
e 3300
 
8.1%
i 2877
 
7.1%
r 2717
 
6.7%
l 2212
 
5.4%
d 1725
 
4.2%
n 1633
 
4.0%
o 1321
 
3.2%
t 1312
 
3.2%
Other values (36) 13679
33.6%

week_day
Categorical

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size38.4 KiB
Saturday
396 
Friday
394 
Sunday
392 
Wednesday
379 
Tuesday
374 
Other values (2)
525 

Length

Max length9
Median length8
Mean length7.1378049
Min length6

Characters and Unicode

Total characters17559
Distinct characters17
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSunday
2nd rowWednesday
3rd rowWednesday
4th rowWednesday
5th rowWednesday

Common Values

ValueCountFrequency (%)
Saturday 396
16.1%
Friday 394
16.0%
Sunday 392
15.9%
Wednesday 379
15.4%
Tuesday 374
15.2%
Monday 277
11.3%
Thursday 248
10.1%

Length

2023-02-03T18:51:18.724902image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-03T18:51:18.828907image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
saturday 396
16.1%
friday 394
16.0%
sunday 392
15.9%
wednesday 379
15.4%
tuesday 374
15.2%
monday 277
11.3%
thursday 248
10.1%

Most occurring characters

ValueCountFrequency (%)
a 2856
16.3%
d 2839
16.2%
y 2460
14.0%
u 1410
8.0%
e 1132
 
6.4%
n 1048
 
6.0%
r 1038
 
5.9%
s 1001
 
5.7%
S 788
 
4.5%
T 622
 
3.5%
Other values (7) 2365
13.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 15099
86.0%
Uppercase Letter 2460
 
14.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 2856
18.9%
d 2839
18.8%
y 2460
16.3%
u 1410
9.3%
e 1132
 
7.5%
n 1048
 
6.9%
r 1038
 
6.9%
s 1001
 
6.6%
t 396
 
2.6%
i 394
 
2.6%
Other values (2) 525
 
3.5%
Uppercase Letter
ValueCountFrequency (%)
S 788
32.0%
T 622
25.3%
F 394
16.0%
W 379
15.4%
M 277
 
11.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 17559
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 2856
16.3%
d 2839
16.2%
y 2460
14.0%
u 1410
8.0%
e 1132
 
6.4%
n 1048
 
6.0%
r 1038
 
5.9%
s 1001
 
5.7%
S 788
 
4.5%
T 622
 
3.5%
Other values (7) 2365
13.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 17559
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 2856
16.3%
d 2839
16.2%
y 2460
14.0%
u 1410
8.0%
e 1132
 
6.4%
n 1048
 
6.0%
r 1038
 
5.9%
s 1001
 
5.7%
S 788
 
4.5%
T 622
 
3.5%
Other values (7) 2365
13.5%

year
Categorical

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size38.4 KiB
2016
2460 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters9840
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2016
2nd row2016
3rd row2016
4th row2016
5th row2016

Common Values

ValueCountFrequency (%)
2016 2460
100.0%

Length

2023-02-03T18:51:19.044904image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-03T18:51:19.120908image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
2016 2460
100.0%

Most occurring characters

ValueCountFrequency (%)
2 2460
25.0%
0 2460
25.0%
1 2460
25.0%
6 2460
25.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9840
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 2460
25.0%
0 2460
25.0%
1 2460
25.0%
6 2460
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9840
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 2460
25.0%
0 2460
25.0%
1 2460
25.0%
6 2460
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9840
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 2460
25.0%
0 2460
25.0%
1 2460
25.0%
6 2460
25.0%

fiedl_type
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size38.4 KiB
on grass
2293 
on turf
 
167

Length

Max length8
Median length8
Mean length7.9321138
Min length7

Characters and Unicode

Total characters19513
Distinct characters10
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowon grass
2nd rowon grass
3rd rowon grass
4th rowon grass
5th rowon grass

Common Values

ValueCountFrequency (%)
on grass 2293
93.2%
on turf 167
 
6.8%

Length

2023-02-03T18:51:19.184908image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-02-03T18:51:19.268906image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
ValueCountFrequency (%)
on 2460
50.0%
grass 2293
46.6%
turf 167
 
3.4%

Most occurring characters

ValueCountFrequency (%)
s 4586
23.5%
o 2460
12.6%
n 2460
12.6%
2460
12.6%
r 2460
12.6%
g 2293
11.8%
a 2293
11.8%
t 167
 
0.9%
u 167
 
0.9%
f 167
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 17053
87.4%
Space Separator 2460
 
12.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
s 4586
26.9%
o 2460
14.4%
n 2460
14.4%
r 2460
14.4%
g 2293
13.4%
a 2293
13.4%
t 167
 
1.0%
u 167
 
1.0%
f 167
 
1.0%
Space Separator
ValueCountFrequency (%)
2460
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 17053
87.4%
Common 2460
 
12.6%

Most frequent character per script

Latin
ValueCountFrequency (%)
s 4586
26.9%
o 2460
14.4%
n 2460
14.4%
r 2460
14.4%
g 2293
13.4%
a 2293
13.4%
t 167
 
1.0%
u 167
 
1.0%
f 167
 
1.0%
Common
ValueCountFrequency (%)
2460
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 19513
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
s 4586
23.5%
o 2460
12.6%
n 2460
12.6%
2460
12.6%
r 2460
12.6%
g 2293
11.8%
a 2293
11.8%
t 167
 
0.9%
u 167
 
0.9%
f 167
 
0.9%

Interactions

2023-02-03T18:51:14.988637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:05.772013image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:06.862725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:07.930751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:09.047376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:10.054033image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:12.845716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:13.974638image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:15.083391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:05.861014image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:06.952727image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:08.014738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:09.132085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:10.334716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:12.932716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:14.059639image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:15.175391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:05.949724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:07.049725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:08.102751image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:09.222085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:10.727716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:13.020715image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:14.147637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:15.268391image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:06.094724image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:07.142725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:08.184750image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:09.306087image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:11.010718image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:13.107716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:14.231637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:15.360065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:06.179725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:07.238725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:08.271441image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:09.391085image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:11.293716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:13.197716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:14.318637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:15.764065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:06.578725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:07.658750image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:08.768113image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:09.786705image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:11.873716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:13.702637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:14.722637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:15.858065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:06.668725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:07.747750image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:08.865725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:09.874704image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:12.274716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:13.791637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:14.809637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:15.949065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:06.761725image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:07.833738image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:08.953376image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:09.959396image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:12.555716image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:13.876637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
2023-02-03T18:51:14.893637image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/

Correlations

2023-02-03T18:51:19.338903image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
attendanceaway_team_errorsaway_team_hitsaway_team_runsgame_durationhome_team_errorshome_team_hitshome_team_runsaway_teamgame_typehome_teamvenueweek_dayfiedl_type
attendance1.0000.018-0.042-0.0490.050-0.0180.0010.0240.1190.1230.4250.4260.1300.402
away_team_errors0.0181.0000.0330.0470.1310.0050.1450.2060.0330.0000.0000.0000.0280.000
away_team_hits-0.0420.0331.0000.7590.4870.1660.1010.0460.0000.0000.0670.0670.0000.000
away_team_runs-0.0490.0470.7591.0000.4580.2570.0870.0350.0300.0000.0640.0620.0000.000
game_duration0.0500.1310.4870.4581.0000.1520.3480.2460.0090.0000.0240.0000.0000.000
home_team_errors-0.0180.0050.1660.2570.1521.000-0.019-0.0100.0360.0000.0490.0460.0000.000
home_team_hits0.0010.1450.1010.0870.348-0.0191.0000.7470.0400.0000.0600.0590.0210.000
home_team_runs0.0240.2060.0460.0350.246-0.0100.7471.0000.0080.0000.0380.0340.0000.000
away_team0.1190.0330.0000.0300.0090.0360.0400.0081.0000.0000.1790.1790.0000.251
game_type0.1230.0000.0000.0000.0000.0000.0000.0000.0001.0000.0980.0970.6170.021
home_team0.4250.0000.0670.0640.0240.0490.0600.0380.1790.0981.0001.0000.0000.994
venue0.4260.0000.0670.0620.0000.0460.0590.0340.1790.0971.0001.0000.0000.994
week_day0.1300.0280.0000.0000.0000.0000.0210.0000.0000.6170.0000.0001.0000.000
fiedl_type0.4020.0000.0000.0000.0000.0000.0000.0000.2510.0210.9940.9940.0001.000

Missing values

2023-02-03T18:51:16.104065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-02-03T18:51:16.467065image/svg+xmlMatplotlib v3.6.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

attendanceaway_teamaway_team_errorsaway_team_hitsaway_team_runsdategame_durationgame_typehome_teamhome_team_errorshome_team_hitshome_team_runsstart_timevenueweek_dayyearfiedl_type
040030.0New York Mets173April 3313Night GameKansas City Royals0947:38pmKauffman StadiumSunday2016on grass
121621.0Philadelphia Phillies052April 6223Night GameCincinnati Reds0837:11pmGreat American Ball ParkWednesday2016on grass
212622.0Minnesota Twins052April 6311Night GameBaltimore Orioles0947:07pmOriole Park at Camden YardsWednesday2016on grass
318531.0Washington Nationals083April 6253Night GameAtlanta Braves1817:10pmTurner FieldWednesday2016on grass
418572.0Colorado Rockies184April 6239Day GameArizona Diamondbacks08312:40pmChase FieldWednesday2016on grass
528386.0Seattle Mariners11110April 5330Night GameTexas Rangers1727:07pmGlobe Life Park in ArlingtonTuesday2016on grass
612757.0Toronto Blue Jays092April 5307Night GameTampa Bay Rays1737:10pmTropicana FieldTuesday2016on turf
728329.0Los Angeles Dodgers063April 5236Night GameSan Diego Padres1207:11pmPetco ParkTuesday2016on grass
826049.0St. Louis Cardinals185April 5327Night GamePittsburgh Pirates21267:08pmPNC ParkTuesday2016on grass
910478.0Chicago White Sox0115April 5328Night GameOakland Athletics01047:08pmOakland-Alameda County ColiseumTuesday2016on grass
attendanceaway_teamaway_team_errorsaway_team_hitsaway_team_runsdategame_durationgame_typehome_teamhome_team_errorshome_team_hitshome_team_runsstart_timevenueweek_dayyearfiedl_type
245343683.0Philadelphia Phillies262April 4256Day GameCincinnati Reds0664:11pmGreat American Ball ParkMonday2016on grass
245445785.0Minnesota Twins072April 4248Day GameBaltimore Orioles01034:46pmOriole Park at Camden YardsMonday2016on grass
245548282.0Washington Nationals084April 4323Day GameAtlanta Braves2434:13pmTurner FieldMonday2016on grass
245648165.0Colorado Rockies01510April 4411Night GameArizona Diamondbacks01256:42pmChase FieldMonday2016on grass
245744020.0Chicago Cubs0119April 4308Night GameLos Angeles Angels of Anaheim1307:08pmAngel Stadium of AnaheimMonday2016on grass
245831042.0Toronto Blue Jays275April 3251Day GameTampa Bay Rays1734:09pmTropicana FieldSunday2016on turf
245939500.0St. Louis Cardinals051April 3302Day GamePittsburgh Pirates1941:15pmPNC ParkSunday2016on grass
246020098.0San Francisco Giants063April 6319Day GameMilwaukee Brewers29412:41pmMiller ParkWednesday2016on grass
246117883.0Detroit Tigers0137April 6322Day GameMiami Marlins11034:57pmMarlins ParkWednesday2016on grass
246210298.0Boston Red Sox1106April 6329Night GameCleveland Indians0976:22pmProgressive FieldWednesday2016on grass